Non-ST Segment Elevation Myocardial Infarction Problem
Clinically, the current measuring method of myocardial infarction still requires elevation or depression ST segment in traditional ECG as the diagnostic criteria, and it has been used for more than half a century. Among all heart-related diseases, CAD, AMI, and ACS attack rates amount to 60% to 70%. Noninvasive ECG examination is the only most common, convenient, and wide-spread method to detect arrhythmia, coronary vascular heart disease and all other cardiac disease; however, low detection rate of CAD and AMI by such examination is the major problem to be dealt with in the world at present.
According to many official sources, the waveforms of ST segment in ECG fall an additional 50% following 70% changes with myocardial ischemia. (See FIG. 7.) Therefore, sudden cardiac death and cardiovascular disease have always been the top causes of mortality and morbidity globally; the main reason may be that there are too many patients whose ST segments in traditional ECG fail to elevation or depression. So far, the change of ST segment has been taken as the standard to determine and diagnose MI & AMI; currently, for CAD and AMI patients whose ST segment does not change, they are all clinically categorized as NSTEMI (non-ST segment elevation myocardial infarction), but they can be deemed as patients with myocardial infarction by laboratory test or other test.
ECG itself is a “qualitative” diagnosis method based on changes of waveform morphology patterns. However, many characteristics of morphological waveform stay unchanged or have no patterns. In the case that the waveform stays unchanged at ST segment, there is a need to establish a standard for Myocardial Infarction without changes of ST segment.
ECG Quantitative Problem
Since the development of ECG, the electrophysiological signal separation of specific time periods of cardiac self-conduction system within the P wave, the QRS complex and the T wave has never been achieved. There is standardized data available in cardiac science, but such quantitative data has never been used in ECG for many reasons. Conventionally, only the character of an ECG waveform is read. As a result, only qualitative information is provided in clinical applications.
ECG is a noninvasive electrophysiological technology. It is able to scan and record cardiac electrical conduction signals. It is the only tracing image of cardiac “bioelectric conduction”, which is the character marker of the life of a heart. However, in the medical field, ECG is only a morphological technique, and the target of its reading, analyzing and determining is waveform character. It is a qualitative technology that does not have quantitative data, which is due to the ECG waveform being associated with deformation and instability. The standard measuring points frequently disappear, hide, overlap and shift. Consequently, they are frequently not shown in ECG, and thus cannot be used for making a determination.
Moreover, conventional ECG cannot measure all digital parameters. The established standards cannot be applied clinically, and cannot be measured. As a result, ECG is unable to achieve a data-based quantitative application. Hence, to date, ECG is still a qualitative application. The foregoing is the reason for which ECG is deemed as a technology that needs experience. However, it is noted that such experience needs to be accumulated from a great number of cases with incorrect diagnosis.
The heart is the most important organ in human body. In addition to heart diseases, a variety of other diseases may also cause abnormalities in heart. Hence, not only cardiologists, but also all doctors in other departments need to read ECG. However, the morphological character changes in ECG cause difficulties for doctors to read ECG. Hence, the issue of how to use the ECG data has confused clinical practices for many years. In traditional ECG, diagnosis of diseases is still made according to morphological character changes in waveforms.
As a result, systems and methods are needed to add quantitative scientific indicators to ECG. Such systems and methods are needed to allow doctors to understand and utilize the knowledge saved in traditional ECG, as well as to reduce learning difficulties, reduce guesses in case diagnosis, reduce misjudgment rate, and improve reliability, diagnosis rate and accuracy, whenever waveform character changes.
ECG Accuracy Problem
Since the first ECG instrument was invented in 1903, its accuracy rate for diagnosis has always been a problem in clinical applications. For people with abnormal conditions, ECG waveform variations are not the same for the same person and are not completely identical even for the same disease. They are at most self-similar. Self-similarly, for example, refers to an object having a shape that is similar to the shape of one of its parts. As a result, ECG science is one of the most complicated disciplines in medicine.
It can be seen from numerous signal processing methods that, during a lifetime, each beat of a person's heart has different specific signal variation, and the difference is significant. However, generally one is unable to observe this from a conventional linear ECG waveform with the naked eye.
Since computers started to be widely used in ECG analysis the 1970s, people have been consistently exploring, searching, and studying how to automate ECG analysis and diagnosis. In the past half a century, thousands of scholars have made efforts in studying algorithms, exploring pattern recognition, and applying those in ECG mapping and automatic diagnosis.
However, wide clinical use of such systems has yet to be achieved. There are at least three technical reasons for this.
1. The ECG waveform is morphological, and generally no consistent mapping points can be found. In other words, the information in the ECG waveform is conveyed through its structure or form. Also, the waveform is abstractly self-similar. In particular, there is no rule for abnormal variations, the time axis signals interfere with each other on left and right sides of as well as above the x-axis, non-linear variations are invisible, and the same disease may have hundreds of millions of variations, but they are not clearly displayed on the ECG waveform. As a result, all ECG parameters are, in general, not accurate, and it is almost impossible to measure these parameters after the waveform changes. Therefore, the highest accuracy of automatic diagnosis by existing ECG software reaches around 38%. Also, this accuracy is only achieved for simple ECG waveform variations and not for many complex waveforms. This is because no mapping point can be found due to the loss or disappearance or deformation of the P-QRS-T waveform.
2. The second reason systems for automated ECG analysis and diagnosis have not been adopted clinically is related to how a conventional ECG waveform has been measured. As described in the '204 Patent and below, the conventional ECG waveform is a single time domain waveform that represents a combination of many different frequency domain signals from different parts of the heart muscle. As a result, information specific to these different parts of the heart muscle are generally lost. In addition, the conventional ECG waveform is a linear waveform, while the heart is a nonlinear system, and the vast majority of variations as a result of abnormality are nonlinear.
3. The third reason systems for automated ECG analysis and diagnosis have not been adopted clinically is related to the high number of false positives found in normal and abnormal populations. For example, in many cases, conventional ECG waveforms show abnormal results in tests of normal people and also show normal results in tests of abnormal people, which makes it extremely difficult for clinical reading and understanding and makes it impossible to determine whether a result is normal or abnormal.
However, the heart is an electrified organ, and there is no doubt that the electrophysiological responses of a heart organ are the fastest and most sensitive measurements to diagnose heart problems. ECG remains one of the most extensively used clinical tools used at present along with blood tests and imaging, despite the lack of accurate systems for automated ECG analysis and diagnosis. As a result, there is a significant need for such systems.
Recent advancements have addressed one of the three technical problems. This is the conventional ECG waveform measurement problem. As described in the '204 Patent and below, an ECG device has been developed that uses signal processing to detect one or more subwaveforms within the P, Q, R, S, T, U, and J waveforms of a conventional ECG waveform and/or within the intervals between the P, Q, R, S, T, U, and J waveforms of a conventional ECG waveform. In other words, the device of the '204 Patent can provide information (subwaveforms) about different frequency domain signals from different parts of the heart muscle. A waveform displaying these subwaveforms is referred to as a saah ECG waveform, for example. In FIG. 30, described below, portions of a saah ECG waveform 3030 and a conventional or traditional ECG waveform 3040 are compared. FIG. 30 shows that saah ECG waveform 3030 relates ECG signals more closely to the anatomy of self-conducting system 3020 than traditional ECG waveform 3040.
As described in the '930 Patent and below, one way the different frequency domain signals from different parts of the heart muscle can be measured is through multi-domain ECG. In multi-domain ECG heart signals are measured using different frequency bands. These multi-domain ECG heart signals can be displayed in one diagram as an electrophysiocardiogram (EPCG) waveform. FIG. 32 shows EPCG waveforms before and after percutaneous coronary intervention (PCI), for example.
As a result of the systems of the '204 Patent and the '930 Patent, the technical problem of measuring the different frequency domain signals from different parts of the heart muscle has been addressed.
ECG Parameter Measurement Problem
The heart beats day and night from the first day of a human life to the last day of life. In a whole life, the heart beats about 2.5 billion to 3 billion times. In this regard, it could be calculated that the heart each time pumps 80 ml blood. Accordingly, based on the fact that the heart beats about 70 times per minute on average, the heart hence pumps 8,000 liters of blood per day, which is equivalent to the volume of 40 barrels of gasoline, and the total weight would be 8 tons. Therefore, the heart pumps 3,000 tons of blood per year. If a person lives for 80 years, the number will reach 240,000 tons. It is noted that after the age of 60 years old, a person has a 45% chance of having the condition of arrhythmia, and about half of those cases become life threatening. According to a variety of different scientific predictions, some scientists believe that it is reasonable to predict that the average lifespan of a person is about 80 years old.
The heart is a charged elastic mucus organ, so an ECG instrument is the only instrument that is able to scan and record the physiological and pathological signs of the cardiac electrophysiology (heart ultrasound provides hemodynamic data, CT & MIR provide histological imaging data). ECG provides electrophysiological signals, in particular noninvasive electrophysiological data. It is able to scan and record cardiac electrical conduction signals. By far, it is the only tool that can record the scanning image of “bioelectric conduction,” the identifier of a living heart.
On the other hand, however, in the medical field, ECG is also only a morphological signal. It needs to be read and analyzed to determine its various waveforms. In this regard, it is an area that highly relies on a practitioner's experience. In the history of ECG, there are many data-based parameter gold standards, such as P-R interval, Q-T interval, ST segment, QRS complex, P-J interval, J-T interval, VAT.
However, due to the fact that ECG waveforms are prone to certain issues such as deformation, instability, standard point loss, and so on, conventional ECG instruments are unable to accurately measure these ECG parameters. As a result, many established standards cannot be applied in clinical practice.
Furthermore, as for the data measured manually, a very small variation can result in a difference of tens of milliseconds. Clinically, only simple standards can be used at present, such as: HR, RR interval, PP interval. As a result, only a few very simple standards can be used in current clinical practice, including HR, RR interval, PP interval, etc.
As described above, the ECG systems of the '204 Patent and the '930 Patent have addressed the technical problem of accurately measuring the different frequency domain signals from different parts of the heart muscle.
Additional systems and methods, however, are needed to accurately measure ECG parameters such as the P-R interval, Q-T interval, ST segment, QRS complex, P-J interval, J-T interval, and VAT so that these standards can be used in clinical practice.
ECG History
Electrical signals produced by a human heart were observed through electrodes attached to a patient's skin as early as 1879. Between 1897 and 1911 various methods were used to detect these electrical signals and record a heartbeat in real-time. In 1924, Willem Einthoven was awarded the Nobel Prize in medicine for identifying the various waveforms of a heartbeat and assigning the letters P, Q, R, S, T, U, and J to these waveforms. Since the early 1900s, the equipment used for electrocardiography (ECG or EKG) has changed. However, the basic waveforms detected and analyzed have not changed.
An ECG device detects electrical impulses or changes in the electrical potential between two electrodes attached to the skin of a patient as the heart muscle contracts or beats. Electrically, the contraction of the heart is caused by depolarization and repolarization of various parts of the heart muscle. Initially, or at rest, the muscle cells of the heart have a negative charge. In order to cause them to contract, they receive an influx of positive ions Na+ and Ca++. This influx of positive ions is called depolarization. The return of negative ions to bring the heart back to a resting state is called repolarization. Depolarization and repolarization of the heart affect different parts of the heart over time giving rise to the P, Q, R, S, T, U, and J waveforms.
FIG. 2 is an exemplary plot 200 of the P, Q, R, S, and T waveforms of a conventional ECG waveform of a heartbeat from a conventional ECG device. The P, Q, R, S, and T waveforms represent electrical conduction through a heart muscle. P waveform 210 represents the propagation of depolarization from the sinoatrial node, to the right and left atriums, and to the atrioventricular node. The sinoatrial node is also referred to as the sinus node, SA node, or SAN. The atrioventricular node is also referred to as the AV node or AVN. The right atrium is also referred to as the RA, and the left atrium is also referred to as the LA.
FIG. 3 is an exemplary diagram 300 of the depolarization of the muscle tissue of a heart that produces P waveform 210 of FIG. 2 as detected by a conventional ECG device. P waveform 210 of FIG. 2 is produced as depolarization propagates from SAN 310 to AVN 340 in FIG. 3. As depolarization propagates from SAN 310 to AVN 340, it also spreads from RA 320 to LA 340. P waveform 210 of FIG. 2 typically has a duration of 80 ms, for example.
PR segment 220 of FIG. 2 represents the propagation of depolarization from the AVN to the Bundle of His, and then to the Bundle Branches. PR segment 230 may also include depolarization to the Purkinje fibers of the inner ventricular walls. The Bundle of His is also referred to as the His Bundle or His. The Bundle Branches include the right bundle branches (RBB) and the left bundle branches (LBB). As shown in FIG. 2, in a conventional ECG, PR segment 220 shows up as a flat line or waveform with no amplitude.
FIG. 4 is an exemplary diagram 400 of the depolarization of the muscle tissue of a heart that produces PR segment 220 of FIG. 2 as detected by a conventional ECG device. PR segment 220 of FIG. 2 is produced as depolarization propagates from AVN 340 to His 450 and then to Bundle Branches 460 that include RBB 461 and LBB 462. PR segment 220 of FIG. 2 typically has a duration of between 50 and 120 ms, for example.
Waveforms Q 230, R 240, and S 250 of FIG. 2 form the QRS complex. The QRS complex represents the propagation of depolarization through the right and left ventricles. The right ventricle is also referred to as RV, and the left ventricle is referred to as LV.
FIG. 5 is an exemplary diagram 500 of the depolarization of the muscle tissue of a heart that produces Q waveform 230, R waveform 240, and S waveform 250 of FIG. 2 as detected by a conventional ECG device. Waveforms Q 230, R 240, and S 250 of FIG. 2 produced as depolarization propagates from Bundle Branches 460 through RV 571 and LV 572. RV 571 and LV 572 have the largest muscle mass in the heart. The QRS complex formed by waveforms Q 230, R 240, and S 250 of FIG. 2 typically has a duration of between 80 and 100 ms, for example.
ST segment 260 of FIG. 2 represents the period during which the ventricles remain depolarized and contracted. As shown in FIG. 2, in a conventional ECG, ST segment 260 shows up as a flat line or waveform with no amplitude. ST segment 260 typically has a duration of between 80 and 120 ms, for example.
The point in FIG. 2 at which the QRS complex ends and ST segment 260 begins is called J point 255. A J waveform (not shown) can sometimes appear as an elevated J point at J point 255 or as a secondary R waveform. A J waveform is usually characteristic of a specific disease. The J waveform is also referred to as the Osborn wave, camel-hump sign, sate delta wave, hathook junction, hypothermic wave, prominent J wave, K wave, H wave or current of injury.
T waveform 270 of FIG. 2 represents the repolarization or recovery of the ventricles. T waveform 270 typically has a duration of 160 ms, for example. The interval between the Q and T waveforms is referred to as the QT interval.
FIG. 6 is an exemplary diagram 600 of the repolarization of the muscle tissue of a heart that produces T waveform 270 of FIG. 2 as detected by a conventional ECG device. As shown in FIG. 6, RV 571 and LV 572 are repolarized.
Not shown in FIG. 2 is the U waveform. The U waveform sometimes appears after the T waveform. The U waveform is thought to represent repolarization of the interventricular septum, the papillary muscles, or the Purkinje fibers.
As shown in FIGS. 3 through 6, as a heart beats, electrical signals flow through all the different muscle tissues of the heart. As shown in FIG. 2, for the last 100 years conventional ECG devices have been able to detect some of these signals in the form of the P, Q, R, S, T, U, and J waveforms. These waveforms have aided in the diagnosis and treatment of many heart problems. Unfortunately, however, the P, Q, R, S, T, U, and J waveforms do not provide a complete picture of the operation of all the different muscle tissues of the heart. As a result, improved systems and methods are needed to detect and analyze more information from the electrical signals that flow through all the different muscle tissues of the heart as it is beating. This additional information can be used to diagnose and treat many more heart problems.
Artificial Intelligence
Artificial Intelligence (AI) generally refers to languages, algorithms, and operating systems that relate to how a computer system can carry out tasks that were previously only completed by relying on human intelligence. It is a general term and often does not include implementation or application. The definition of AI has evolved over time, however, and this phenomenon is referred to as the “AI effect.” The AI effect can be summarized as the prescription that “AI intends to complete a collection of all tasks that cannot be implemented without relying on human intelligence at the present.” In the 1940s and 1950s, a group of scientists from different fields (mathematics, psychology, engineering, economics and politics) began to explore the possibility of manufacturing an artificial brain. In 1956, AI was established as a discipline. The organizers of the 1956 Dartmouth Artificial Intelligence Conference were Marvin Minsky, John McCarthy, and two other senior scientists, Claude Shannon and Nathan Rochester, with the latter coming from IBM. At the 1956 Dartmouth Artificial Intelligence Conference, the name and tasks of AI were determined, and at the same time, initial achievements and the earliest group of researchers appeared. As a result, this event has been extensively acknowledged as a sign of the birth of AI. It is clear that AI is now a technological field, a second revolution since the invention of the computer, and a certain trend in the future. It is being applied in all industries, exists everywhere, and is used on almost everything on the earth. In the medical field, AI is now used in the following: medical imaging, sensor-based data analysis, conversion of bioinformatics, and development of public health policies. AI is also used in the clinical applications. These applications include cancer treatment: recognition of mitosis of cancerous tumor cells, identification of disease types and degrees of aggravation, shortening chemotherapy time, and mitigating damage caused by chemotherapy for cancer patients. These applications also include ophthalmological diagnosis: recognition of early signs of eye disease, such as senile macular degeneration, and diabetic retinopathy and surgical treatment: AI surgical robots, etc. Google has also formed a team called DeepMind Health, which cooperated with the Imperial College London and the Royal Free Hospital in London, UK. They released a mobile application called Streams, and medical professionals can use Streams to observe treatment results in a faster manner. Overall, in the medical field, the AI system can be used on any job that previously required human thinking.